code: def __init__(
      self,
      logical_operator,
      weights,
      biases,
      activation,
      classes,
      device,
  ):
      super(MLPClassificationModel, self).__init__(
          logical_operator, weights, biases, activation, device
      )
      self.regression = False
      self.classification = True
      self.classes = torch.nn.Parameter(
          torch.IntTensor(classes), requires_grad=False
      )
      self.perform_class_select = False
  
      if min(classes) != 0 or max(classes) != len(classes) - 1:
          self.perform_class_select = True
  
      self.binary_classification = False
      if len(classes) == 2:
          self.binary_classification = True
  
file_path: hummingbird/hummingbird/ml/operator_converters/_mlp_implementations.py
imports: import torch
  from ._physical_operator import PhysicalOperator
label_desc: Use image node_id 1 to create a new MLPClassificationModel object from inherited base classes: MLPModel with example: obj = MLPClassificationModel(logical_operator, weights, biases, activation, classes, device)